CIT protects performance in IB, AP, IGCSE and international-school courses first. Students then extend the same knowledge into a separate project with a new question, new evidence and clear student ownership: for KSEF, coding competitions, service, or a defensible technical portfolio.
CIT is an AI and coding academy in Apgujeong, Seoul, teaching in English and Korean. We do not duplicate assessed submissions. Evidence rules vary by university, admissions route and year; awards and admission are not guaranteed.
Protect AP, IB and IGCSE academics first, then build student-owned AI projects and EC evidence for SIS, SFS, YISS, KIS, APIS and peers. Onsite in Apgujeong and online across Korea and overseas.
Check school and cycle-specific rules first, then document student-owned inquiry without implying a universal admissions submission. Online across Korea and overseas.
Build a separate project from what a student already learns: new question, new evidence, student ownership, and route-compatible documentation.
Twenty-one simulation-first courses based on pinned public research repositories, including the existing RoboMaster course.
The Korean AI Olympiad track: 11 stages from foundations to portfolio, mock exams, and national-selection interview coaching.
USA AI Olympiad track for US/Canada-eligible students: diagnosis, math and Colab foundations, Round 1 and 2 preparation.
International AI Innovation Olympiad: AIIC exam, AI4SCI research, and AI4BIZ tracks, open to students of any nationality.
Exam-focused AP Computer Science preparation; see also IB / IGCSE CS on the programs index.
Documenting student-owned work, methods, limitations and verification in formats permitted by the student's application route.
All programs → · All courses → · AI+X field guide → · Free online level tests (USACO · AP CS · IGCSE · KOAI · USAAIO) →
Students do not copy an entire repository. They read a focused part of real research code, change one condition, then document their prediction, result, errors and limits in their own words.
A MuJoCo-powered simulation framework and reproducible benchmark for robot learning.
Student inquiry: change one control frequency and compare how the same command behaves against a baseline.
A C++ and Python toolbox for analyzing robot dynamics and designing and verifying control systems.
Student inquiry: locate where state is stored and explain how changing one control-block connection affects the result.
A photorealistic simulated environment for testing autonomous-drone flight with reinforcement learning.
Student inquiry: ask how obstacle and environment conditions change success rate, flight time and energy use.
University and lab names identify the source of each public project. CIT independently designed these learning paths; they are not official courses, partnerships or endorsements from those institutions. Images are GitHub repository previews captured at review time, so displayed counts may change.
USACO, AI olympiads, and research challenges: eligibility mapping, training plans, and mock rounds calibrated to each competition's real format.
Student-owned work documented with the exact role, methods, evidence, limitations, artifacts, and support a student can truthfully explain.
Academic and project planning informed by route-specific evidence rules for Korea, the US, and the UK, while the student owns the work.
We map academic priorities, the assessed-work boundary, a realistic independent extension and route-compatible evidence before recommending a competition or portfolio outcome.